Added new category plot. Added descriptive comments

src/config.py

@@ -6,9 +6,15 @@ Created on Nov 20, 2012
 
 DATA_DIR = '/Users/abuddenberg/workspace/plot_precip_maps/data/'
 NA_PRECIP_FILES = [
-                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp85_2071-2099_NA_percent_change_grads15.eps'),
-                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp26_2071-2099_NA_percent_change_grads15.eps'),
-                (DATA_DIR + 'pr_sresa2_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_sresa2_2071-2099_NA_percent_change_grads15.eps')
+                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp85_2071-2099_NA_percent_change.eps'),
+                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp26_2071-2099_NA_percent_change.eps'),
+                (DATA_DIR + 'pr_sresa2_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_sresa2_2071-2099_NA_percent_change.eps')
+                ]
+
+NA_CATEGORY_FILES = [
+                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp85_2071-2099_NA_percent_change_categories.eps'),
+                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp26_2071-2099_NA_percent_change_categories.eps'),
+                (DATA_DIR + 'pr_sresa2_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_sresa2_2071-2099_NA_percent_change_categories.eps')                  
                 ]
 
 GLOBAL_PRECIP_FILES = [

src/plot_global_precip.py

@@ -2,6 +2,13 @@
 Created on Nov 21, 2012
 
 @author: abuddenberg
+
+Creates a  contour plot of projected precipitation change
+globally for each emmissions scenario at the end of the
+21st century vs the base period (presumably 1971-1999).
+
+The hatched area denotes areas of higher statistical significance. 
+
 '''
 from scipy.io.netcdf import netcdf_file
 from mpl_toolkits.basemap import Basemap
@@ -33,7 +40,6 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
         )
         
         m.drawcoastlines()
-#        m.drawstates()
         m.drawcountries()
         
         lons, lats = meshgrid(lon_data, lat_data)

src/plot_north_american_categories.py

+'''
+Created on Nov 29, 2012
+
+@author: abuddenberg
+
+The purpose of this program is to plot the categories of statistical
+significance for the various emissions scenarios in the North American
+area.  This plot is used in conjection with the accompanying contour plots
+to spot-check anomalies caused by the smoothing in those plots (as well as to
+verify the data itself).
+
+The categories are as follows:
+
+1.0 denotes areas little change
+2.0 denotes areas of statistical uncertainty
+3.0 denotes areas of statistical significance
+
+Fun stuff
+'''
+from scipy.io.netcdf import netcdf_file
+from mpl_toolkits.basemap import Basemap
+from numpy import meshgrid
+from numpy.ma import masked_equal
+import numpy as np
+
+import matplotlib.pyplot as plt
+from config import NA_CATEGORY_FILES, SEASONS
+
+for infilename, outfilename in NA_CATEGORY_FILES:
+    nc = netcdf_file(infilename) 
+    
+    lat_data = nc.variables['lat'].data 
+    lon_data = nc.variables['lon'].data - 360.
+    
+    fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
+    
+    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
+        data_var, signif_var = SEASONS[season]
+        
+        data = nc.variables[data_var].data
+        signif = nc.variables[signif_var].data
+        
+        ax = fig.add_subplot(221 + i)
+        plt.title(season)
+        m = Basemap(
+            projection='aea',
+                    lon_0=-96,
+                    lat_0=37.5,
+                    lat_1=29.5,
+                    lat_2=45.5,
+        #            lat_ts=median(lats),
+            llcrnrlat=12,
+            urcrnrlat=80,
+            llcrnrlon=-135,
+            urcrnrlon=-25,
+            resolution='l',area_thresh=10000
+        )
+        
+        m.drawcoastlines()
+        m.drawstates()
+        m.drawcountries()
+        
+        lons, lats = meshgrid(lon_data, lat_data)
+        x,y = m(lons, lats)
+    
+        #Build boolean masks of the gridpoint for each category
+        stipples_mask = np.ma.getmask(np.ma.masked_equal(signif, 1.))
+        zeros_mask = np.ma.getmask(masked_equal(data, 0.0))
+
+        both_mask = np.ma.mask_or(stipples_mask, zeros_mask)
+        third_cat_mask = ~both_mask
+        
+        #There's got to be a better way of doing this than copying the array
+        data = np.ma.masked_array(data)
+        data.mask = stipples_mask
+        data = np.ma.masked_array(data.filled(3.0)) #3.0 denotes areas of statistical significance
+        
+        data.mask = zeros_mask
+        data = np.ma.masked_array(data.filled(1.0)) #1.0 denotes areas little change
+                
+        data.mask = third_cat_mask
+        data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty
+        
+ 
+        weird = m.pcolor(x,y, data)
+        m.colorbar(weird,location='right',pad="5%")
+        
+        #Tests for overlap (There shouldn't be any)
+#        print np.any(np.logical_and(stipples_mask, zeros_mask))
+#        print np.any(np.logical_and(third_cat_mask, zeros_mask))
+#        print np.any(np.logical_and(third_cat_mask, stipples_mask))
+        
+    
+    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
+#    plt.show()
\ No newline at end of file

src/plot_north_american_precip.py

@@ -2,6 +2,13 @@
 Created on Nov 16, 2012
 
 @author: abuddenberg
+
+Creates a very lovely contour plot of projected precipitation change
+over North American for each emmissions scenario at the end of the
+21st century vs the base period (presumably 1971-1999).
+
+The hatched area denotes areas of higher statistical significance.   
+
 '''
 from scipy.io.netcdf import netcdf_file
 from mpl_toolkits.basemap import Basemap
@@ -51,7 +58,7 @@ for infilename, outfilename in NA_PRECIP_FILES:
         levels_15 = [-45, -30, -15, 0, 15, 30, 45]
         levels_10 = [-30, -20, -10, 0, 10, 20, 30]
         
-        prcp = m.contourf(x, y, data, cmap=plt.get_cmap('BrBG'), levels=levels_15, extend='both')   
+        prcp = m.contourf(x, y, data, cmap=plt.get_cmap('BrBG'), levels=levels_10, extend='both')   
         m.colorbar(prcp, location='bottom',pad="5%")
 
         hatching = m.contourf(x,y, signif, 1, colors='none',hatches=[None, '//'])